Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Morphological Image Analysis: Principles and Applications
Morphological Image Analysis: Principles and Applications
The Journal of Machine Learning Research
Learning to Detect Natural Image Boundaries Using Local Brightness, Color, and Texture Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Categorization by Learning and Reasoning with Regions
The Journal of Machine Learning Research
Morphological segmentation on learned boundaries
Image and Vision Computing
Morphological distinguished regions
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Queue and priority queue based algorithms for computing the quasi-distance transform
ICIAR'10 Proceedings of the 7th international conference on Image Analysis and Recognition - Volume Part I
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We present an image segmentation technique using the morphological Waterfall algorithm. Improvements in the segmentation are brought about by using improved gradients. These are based on the detection of object boundaries learnt from human segmentations introduced by Martin et al. (2004). We avoid the usual pitfall found when applying Watershed algorithms to these boundaries, namely that the boundary lines usually contain gaps, by making use of distance functions on the boundary image. Two types of distance function are used: the classic distance function and a distance function for numerical images recently introduced by Beucher (2005). Resulting segmentations are compared to human segmentations using the Berkeley segmentation benchmark. The benchmark results show that the proposed segmentation algorithm produces segmentations comparable to those produced by the Normalised Cuts algorithm.